Automated artificial intelligence (ai) inspection of customized part production

Abstract

Aspects of the present disclosure relate generally to machine inspection of part production and, more particularly, to systems and methods of automated Al inspection of customized part production. For example, a computer-implemented method includes receiving, by a processor, design information for a custom part; extracting, by the processor, feature information of the custom part from the design information; receiving, by the processor, images of the custom part in production from a recording in near real time; and verifying, by the processor, using machine learning that features in the images of the custom part in production are in compliance with the feature information of the custom part.

What is claimed is: 
     
         1 . A method comprising:
 receiving, by a processor, design information for a custom part;   extracting, by the processor, feature information of the custom part from the design information;   receiving, by the processor, images of the custom part in production from a recording in near real time; and   verifying, by the processor, using machine learning, that features in the images of the custom part in production are in compliance with the feature information of the custom part from the design information.   
     
     
         2 . The method of  claim 1 , further comprising selecting, by the processor, reference images from the images of the custom part in production that are in compliance with the feature information of the custom part. 
     
     
         3 . The method of  claim 1 , further comprising:
 receiving, by the processor, the recording in near real time, wherein the recording is a video recording and is an initial video recording of the custom part in production; and   outputting, by the processor, an alert to a computing device when a feature in the images of the custom part in production is not in compliance with the feature information of the custom part.   
     
     
         4 . The method of  claim 1 , wherein the machine learning comprises applying a convolutional neural network model that identifies subprocesses of the custom part in production from the images of the custom part in production from the recording. 
     
     
         5 . The method of  claim 1 , wherein the machine learning comprises applying a convolutional neural network model that determines the features in the images of the custom part in production are in compliance with an image from the feature information of the custom part. 
     
     
         6 . The method of  claim 4 , wherein the convolutional neural network model is trained to receive the images of the custom part in production from the recording, the subprocesses from a reference video and reference images for each subprocess to identify plural subprocesses of the custom part in production from the images of the custom part in production from the recording. 
     
     
         7 . The method of  claim 5 , wherein the convolutional neural network model is trained to determine that the features in the images of the custom part in production are in compliance using at least one image of the custom part with plural features that are not in compliance with the features of the custom part and at least one image of the custom part with the features that are in compliance with the features of the custom part. 
     
     
         8 . The method of  claim 1 , wherein the verifying using the machine learning comprises applying plural convolutional neural network models that determine the features in the images of the custom part in production are in compliance with an image from the feature information of the custom part in subprocesses of the production. 
     
     
         9 . The method of  claim 2 , further comprising:
 verifying, by the processor, using the machine learning in a subsequent production of another custom part that plural features in images of the another custom part in the subsequent production are in compliance with plural features of the reference images.   
     
     
         10 . The method of  claim 1 , further comprising:
 storing the recording in a storage system; and   inputting images of the custom part in production from the stored recording in a convolutional neural network model that determines plural features in plural images of another custom part in a subsequent production are in compliance with plural features in the images of the custom part in production from the recording.   
     
     
         11 . A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
 receive, by a processor, images of a custom part in production from an initial video recording in near real time;   input, by the processor, the images of the custom part in production into a convolutional neural network model that determines features in the images of the custom part are similar with feature information of the custom part;   receive, by the processor, an indication from the convolutional neural network model that a feature in the images of the custom part in production is not similar with the feature information of the custom part; and   output, by the processor, an alert to a computing device in response to receiving the indication that the custom part in production is not in compliance with the feature information of the custom part.   
     
     
         12 . The computer program product of  claim 11 , wherein the program instructions are further executable to:
 receive, by the processor, design information for the custom part; and   extract, by the processor, the feature information of the custom part from the design information.   
     
     
         13 . The computer program product of  claim 11 , wherein the convolutional neural network model is trained to receive the images of the custom part in production from the initial video recording, subprocesses of the production of the custom part from a reference video and reference images for each subprocess to identify plural subprocesses of the custom part in production from the images of the custom part in production from the initial video recording. 
     
     
         14 . The computer program product of  claim 11 , wherein the convolutional neural network model is trained to receive the images of the custom part in production from the initial video recording and subprocesses of the production of the custom part from a reference video to determine the features in the images of the custom part are similar with the feature information of the custom part. 
     
     
         15 . The computer program product of  claim 11 , wherein the program instructions are further executable to:
 store the initial video recording in a storage system; and   input images of the custom part in the production from the stored initial video recording in a convolutional neural network model that determines plural features in plural images of another custom part in a subsequent production are in compliance with plural features in the images of the custom part in production from the initial video recording.   
     
     
         16 . A system comprising:
 a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:   receive, by the processor, images of a custom part in production from an initial video recording in near real time;   input, by the processor, the images of the custom part in production into a machine learning model that determines features in the images of the custom part are similar with feature information of the custom part;   receive, by the processor, an indication from the machine learning model that a feature in the images of the custom part in production is not similar with the feature information of the custom part; and   output, by the processor, an alert to a computing device in response to receiving the indication that the custom part in production is not in compliance with the feature information of the custom part.   
     
     
         17 . The system of  claim 16 , wherein the program instructions are further executable to:
 receive, by the processor, design information for the custom part; and   extract, by the processor, the feature information of the custom part from the design information.   
     
     
         18 . The system of  claim 16 , wherein the machine learning model comprises a convolutional neural network model that determines the features in the images of the custom part in production are similar with the feature information of the custom part. 
     
     
         19 . The system of  claim 16 , wherein the machine learning model is trained to receive the images of the custom part in production from the initial video recording and subprocesses of the production of the custom part from a reference video to determine the features in the images of the custom part are similar with the feature information of the custom part. 
     
     
         20 . The system of  claim 16 , wherein the program instructions are further executable to:
 store the initial video recording in a storage system; and   input images of the custom part in production from the stored initial video recording in the machine learning model that determines plural features in plural images of another custom part in a subsequent production are similar with plural features in the images of the custom part in production from the initial video recording.